In the paper, we proposed a system for automatic detection and tracking of multiple targets in a multi-camera surveillance zone. In each camera view of this system, we only need a simple object detection algorithm, such as background subtraction. The detection results from multiple cameras are fused into a posterior distribution, named TDP, based on the Bayesian rule. This TDP distribution indicates the likelihood of having some moving elements on the ground plane. To properly handle the tracking of multiple moving targets over time, a sample-based framework, which combines Markov Chain Monte Carlo (MCMC), Sequential Monte Carlo (SMC), and Mean-Shift clustering, is proposed. The MCMC is used to handle the occurrence of new targets. The SMC is used to track existing targets over time. The Mean-Shift clustering is adopted to automatically identify new comers. With the Monte Carlo based framework, the detection and tracking of multiple targets can be achieved in a unified and seamless manner. The detection and tracking accuracy is evaluated by both synthesized videos and real videos. The experimental results show that the proposed system can successfully track a varying number of people accurately.